Evolving meta-level reasoning with reinforcement learning and A* for coordinated multi-agent path-planning

Mona Alshehri; Napoleon Reyes; Andre Barczak

doi:10.5555/3398761.3398968

Evolving meta-level reasoning with reinforcement learning and A* for coordinated multi-agent path-planning

Mona Alshehri, Napoleon Reyes, Andre Barczak

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

5 Citations (Scopus)

Abstract

This work presents an extension to a graph-based evolutionary algorithm, called Genetic Network Programming with Reinforcement Learning (GNP-RL) to make it more amenable for solving coordinated multi-agent path-planning tasks in dynamic environments. We improve the algorithm's ability to evolve meta-level reasoning strategies in three aspects: genetic composition, search and learning strategies, using optimal search algorithm, constraint conformance and task prioritization techniques.

Original language	English
Title of host publication	Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
Editors	Bo An, Amal El Fallah Seghrouchni, Gita Sukthankar
Publisher	International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages	1744-1746
Number of pages	3
ISBN (Electronic)	9781450375184
DOIs	https://doi.org/10.5555/3398761.3398968
Publication status	Published - 2020
Externally published	Yes
Event	19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020 - Virtual, Auckland, New Zealand Duration: 9 May 2020 → 13 May 2020 Conference number: 19th

Publication series

Name	Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume	2020-May
ISSN (Print)	1548-8403
ISSN (Electronic)	1558-2914

Conference

Conference	19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
Abbreviated title	AAMAS
Country/Territory	New Zealand
City	Virtual, Auckland
Period	9/05/20 → 13/05/20

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10.5555/3398761.3398968

Cite this

Alshehri, M., Reyes, N., & Barczak, A. (2020). Evolving meta-level reasoning with reinforcement learning and A* for coordinated multi-agent path-planning. In B. An, A. El Fallah Seghrouchni, & G. Sukthankar (Eds.), Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020 (pp. 1744-1746). (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS; Vol. 2020-May). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). https://doi.org/10.5555/3398761.3398968

Alshehri, Mona ; Reyes, Napoleon ; Barczak, Andre. / Evolving meta-level reasoning with reinforcement learning and A* for coordinated multi-agent path-planning. Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020. editor / Bo An ; Amal El Fallah Seghrouchni ; Gita Sukthankar. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2020. pp. 1744-1746 (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS).

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title = "Evolving meta-level reasoning with reinforcement learning and A* for coordinated multi-agent path-planning",

abstract = "This work presents an extension to a graph-based evolutionary algorithm, called Genetic Network Programming with Reinforcement Learning (GNP-RL) to make it more amenable for solving coordinated multi-agent path-planning tasks in dynamic environments. We improve the algorithm's ability to evolve meta-level reasoning strategies in three aspects: genetic composition, search and learning strategies, using optimal search algorithm, constraint conformance and task prioritization techniques.",

author = "Mona Alshehri and Napoleon Reyes and Andre Barczak",

note = "Publisher Copyright: {\textcopyright} 2020 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved.; 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020, AAMAS ; Conference date: 09-05-2020 Through 13-05-2020",

year = "2020",

doi = "10.5555/3398761.3398968",

language = "English",

series = "Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS",

publisher = "International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)",

pages = "1744--1746",

editor = "Bo An and {El Fallah Seghrouchni}, Amal and Gita Sukthankar",

booktitle = "Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020",

}

Alshehri, M, Reyes, N & Barczak, A 2020, Evolving meta-level reasoning with reinforcement learning and A* for coordinated multi-agent path-planning. in B An, A El Fallah Seghrouchni & G Sukthankar (eds), Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020. Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, vol. 2020-May, International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), pp. 1744-1746, 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020, Virtual, Auckland, New Zealand, 9/05/20. https://doi.org/10.5555/3398761.3398968

Evolving meta-level reasoning with reinforcement learning and A* for coordinated multi-agent path-planning. / Alshehri, Mona; Reyes, Napoleon; Barczak, Andre.
Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020. ed. / Bo An; Amal El Fallah Seghrouchni; Gita Sukthankar. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2020. p. 1744-1746 (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS; Vol. 2020-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

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AU - Barczak, Andre

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PY - 2020

Y1 - 2020

N2 - This work presents an extension to a graph-based evolutionary algorithm, called Genetic Network Programming with Reinforcement Learning (GNP-RL) to make it more amenable for solving coordinated multi-agent path-planning tasks in dynamic environments. We improve the algorithm's ability to evolve meta-level reasoning strategies in three aspects: genetic composition, search and learning strategies, using optimal search algorithm, constraint conformance and task prioritization techniques.

AB - This work presents an extension to a graph-based evolutionary algorithm, called Genetic Network Programming with Reinforcement Learning (GNP-RL) to make it more amenable for solving coordinated multi-agent path-planning tasks in dynamic environments. We improve the algorithm's ability to evolve meta-level reasoning strategies in three aspects: genetic composition, search and learning strategies, using optimal search algorithm, constraint conformance and task prioritization techniques.

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Alshehri M, Reyes N, Barczak A. Evolving meta-level reasoning with reinforcement learning and A* for coordinated multi-agent path-planning. In An B, El Fallah Seghrouchni A, Sukthankar G, editors, Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). 2020. p. 1744-1746. (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS). doi: 10.5555/3398761.3398968

Evolving meta-level reasoning with reinforcement learning and A* for coordinated multi-agent path-planning

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